Optical Wind Lidar-Based Multifunctional Instrument for Enhanced Measurements and Prediction of Clear Air Turbulence and Other Wind-Based Aviation Related Phenomena

ABSTRACT

A multiple functional instrument is provided. The instrument includes an optical autocovariance function interferometer that can feature multiple fields of view to detect winds in the atmosphere. The instrument can include an infrared camera to detect atmospheric temperatures and the presence of clouds, and a detector assembly that detects the polarization of light returned to the interferometer. Data collected by the instrument can be provided to a deep and reinforcement learning algorithm for real-time prediction of clear air turbulence and other wind-based aviation safety phenomena. Moreover, predicted and actual conditions can be correlated and used to train a deep learning algorithm to enable more accurate predictions. The instrument can be carried by an aircraft or other platform and operated to detect clear air turbulence or other atmospheric phenomena, and to provide instructions regarding flight parameters including wind-aided navigation in order to minimize the effect of predicted turbulence.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/723,675, filed Aug. 28, 2018, and the benefit ofU.S. Provisional Patent Application Ser. No. 62/723,690, filed Aug. 28,2018, the entire disclosures of which are hereby incorporated herein byreference.

FIELD

The present disclosure is directed to systems and methods for measuringand predicting wind-based aviation safety phenomena including providingwind-aided navigation to an aircraft based on data from multiplesources.

BACKGROUND

Severe wind conditions such as clear air turbulence encounters bygeneral and commercial aviation continue to pose significant safety andflight efficiency concerns. Almost anyone who has flown commercially hashad an unpleasant experience with turbulence and has a tale to tellabout it. According to some estimates, turbulence encounters account forwell over 75% of all weather-related injuries on commercial aircraft andamount to at least $200M annually in costs due to passenger and crewinjuries and aircraft damage. Consequently, there is an urgent need toprovide accurate and real-time wind and turbulence predictions andcourses-of-action to meet the safety and navigation needs of aviationcommunities.

However the real-time information about the current turbulent state ofthe atmosphere required by pilots and dispatchers for making tacticalen-route decisions is not adequately provided via the Federal AviationAdministration's (FAA's) thunderstorm avoidance guidelines, by currentlyoperational turbulence forecasts, or future systems such as theGraphical Turbulence Guidance (GTG) “Nowcast” (N-GTG) at the NationalCenter for Atmospheric Research (NCAR), which is slated to combineturbulence observations, inferences and forecasts to produce newturbulence assessments approximately every 15 minutes.

Moreover, despite the success of machine learning in a variety of tasks,applications to the problem of weather forecasting have been limited.Exceptions include the use of Bayesian Networks for precipitationforecasts and temporal modeling via Restricted Boltzmann Machines (RBM).To date, uses of machine learning for weather prediction have beenlimited in that almost all methods consider only one variable at a timeand do not explore the joint spatiotemporal statistics of multipleweather phenomena.

Light detection and ranging (lidar) systems have been developed that arecapable of remotely measuring range-resolved wind speeds for use invarious applications, including but not limited to wind-aided navigationof a platform, weather forecasting, air quality prediction, air-trafficsafety, and climate studies. In general, lidar operates by transmittinglight from a laser source to a volume or surface of interest anddetecting the time of flight for the backscattered light to determine arange to the scattering volume or surface.

A Doppler wind lidar also measures the Doppler frequency shiftexperienced by the light scattered back to the instrument due to themotions of molecules and aerosols (e.g. particles and droplets) in theatmospheric scattering volumes, which is directly tied to the speed ofthe wind in that volume, relative to the lidar line of sight (LOS). Thewind speed along the LOS is determined by projecting the wind speed anddirection (the wind vector) onto that LOS.

One potential application for wind lidar systems is in connection withthe detection of atmospheric turbulence and wind shear. As noted,atmospheric turbulence is a primary cause of weather related injuries toaircraft passengers and flight crews. Accordingly, detecting atmosphericturbulence is of great interest. However, systems for detectingturbulence, and in particular clear air turbulence, that can be carriedby aircraft have been unavailable. In particular, a system that wascompact and that provided a suitably wide field of view that could bedeployed in a conventional aircraft has been unavailable.

Moreover, most wind measurements consist of a single wind Doppler lidarinstrument. Such instruments generally have a narrow field of view(FOV), limiting the area of surveillance. Additionally, such instrumentsconsist of a single wavelength, which limits the data diversity forincreasing the accuracy of aviation safety weather-related predictions.

SUMMARY

Embodiments of the present disclosure overcome the limitations describedabove by providing systems and methods incorporating a multifunctionalinstrument that includes an optical autocovariance wind lidar (OAWL)based instrument. In accordance with at least some embodiments of thepresent disclosure, the wind lidar based instrument is configured toperform wind measurements. The wind lidar based instrument can also makemeasurements of aerosol concentrations. In accordance with furtherembodiments of the present disclosure, the multifunctional instrumentincludes a camera or wide field of view infrared (IR) sensor for thermalmeasurement of atmospheric behavior. The multifunctional instrument canalso include one or more on-board accelerometers, which can be used tocompare turbulence predictions to turbulence actually encountered by anaircraft. As used herein, aircraft can include, but are not limited to,airplanes, helicopters, airships (including blimps), gliders, hot airballoons, stratospheric balloons, and Unmanned Aerial Vehicles (UAVs) Inaccordance with further embodiments of the present disclosure, amultifunctional instrument is provided that includes a lidar system thatis capable of obtaining wind speed measurements and aerosol/particleconcentrations from multiple lines of sight. Moreover, in accordancewith at least some embodiments of the present disclosure, measurementsfrom multiple lines of sight can be made simultaneously. Alternativelyor in addition, a lidar capable of making simultaneous measurements overmultiple lines of sight as described herein can include aninterferometer that is configured to operate at multiple wavelengths,and/or that can make wind and aerosol concentration measurementssimultaneously.

In accordance with still further embodiments of the present disclosure,a multifunctional instrument is provided that incorporates a processorand a deep learning algorithm. The deep learning algorithm can beoperated to collect, fuse, and correlate data generated by the lidaralone or by the lidar and other sensors included in the multifunctionalinstrument, to provide predictions regarding turbulence in theatmosphere. Moreover, the deep learning algorithm can be operated toalter or suggest alterations in the course of an aircraft carrying themultifunctional instrument, or of other aircraft.

Further embodiments of the present disclosure overcome the limitationsdescribed above by providing unique and novel methods for combining datafusion of multi-source information on which the latest in artificialintelligence-based deep and reinforcement learning processing algorithmsare applied in a hybrid model to provide accurate and real-time windpredictions for wind-aided navigation of a platform, turbulencepredictions, and courses-of-actions to meet the needs of aviationcommunities.

Additional features and advantages of embodiments of the disclosedsystems and methods will become more readily apparent from the followingdescription, particularly when taken together with the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an aircraft carrying a multifunctional instrument inaccordance with embodiments of the present disclosure;

FIG. 2 depicts components of a multifunctional instrument in accordancewith embodiments of the present disclosure;

FIG. 3 depicts components of an interferometer in accordance withembodiments of the present disclosure; and

FIG. 4 depicts aspects of a process for applying deep learningprocessing to detect and predict turbulence and other atmosphericconditions in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION

FIG. 1 depicts an aircraft 100 carrying a multifunctional instrument orsystem 104 in accordance with embodiments of the present disclosure. Asused herein, an aircraft 100 can include, but is not limited to, anairplane, an airship, a blimp, a glider, a hot air balloon, astratospheric balloon, a helicopter, and an unmanned aerial vehicle(UAV). The multifunctional instrument 104 is capable of obtainingatmospheric measurements from within different fields of regard 108. Forexample, and as discussed in greater detail elsewhere herein, one ormore lidars incorporating an optical autocovariance interferometer canbe included in the multifunctional instrument 104 to obtain relativeline of sight wind speeds from selected ranges within different fieldsof regard 108 that intersect different target volumes 112. In addition,a wide field of view (WFOV) infrared (IR) camera can be included in themultifunctional instrument 104 for obtaining temperature information atdifferent locations within that device's field of view 114.

More particularly, a lidar system included in a multifunctionalinstrument 104 in accordance with embodiments of the present disclosurecan have multiple fields of regard 108, from which relative line ofsight wind speeds can be obtained at selected ranges from themultifunctional instrument 104. These different fields of regard 108 caninclude a forward looking field of regard 108 a, a downward lookingfield of regard 108 b, and an upward looking field of regard 108 c.Although the different fields of regard 108 depicted in the figure areshown at a spacing of approximately 90 degrees from one another,different spacings are possible. For example, the downward 108 b andupward 108 c facing fields of regard 108 can be at angles of less than90 degrees from the forward-looking field of regard 108 a. Moreover,additional fields of regard, including side looking fields of regard, orfields of regard spaced at angles of greater than 90 degrees, can beprovided. As can be appreciated by one of skill in the art afterconsideration of the present disclosure, the lidar system operates totransmit a beam of light as an output signal or beam 116 along or withina corresponding field of view. The transmitted beam can be scanned orvaried in angle relative to the multifunctional instrument 104 tocollect data from within the field of regard 108. Alternatively or inaddition, a lidar system included in the multifunctional instrument 104can comprise an imaging or flash lidar with a relatively large field ofview that is coincident with a corresponding field of regard 108, orthat can be scanned within the field of regard 108.

Particles in the atmosphere along the path of the transmitted lightreflect that light back to an interferometer included in the lidarsystem. For example, at high altitudes (e.g. above 20 km), moleculeswithin a target volume 112 in the atmosphere will backscatter at leastsome of the transmitted light as a return signal 120. At lower altitudes(e.g. below 20 km) molecules and aerosols within a target volume 112 inthe atmosphere will backscatter at least some of the transmitted lightas a return signal 120. The return signal 120 comprising at least someof the backscattered light is received by the lidar system included inthe multifunctional instrument 104, and any Doppler shift experienced bythe light as a result of a relative line of sight wind speed at a rangecorresponding to a target volume 112 can then be detected, to determinethe relative line of sight windspeed within that target volume 112. Thisinformation can then be used to detect the presence of turbulence 124,including but not limited to clear air turbulence, in the target volume112, and to obtain wind measurements that can be used for wind-aidednavigation of the platform, weather forecasting, and the like. Moreover,wind profiles based on wind measurements made by the multifunctionalinstrument 104 at the aircraft 100 level and below can be provided toglobal and local weather forecasting offices and systems in nearreal-time to improve forecast model initialization.

In accordance with further embodiments of the present disclosure, thepolarization of light in the return signal 120 can be used, alone or incombination with information received from other sensors, to detect thepresence of ice, ash, or dust particles within the target volume 112.Although the detection of turbulence and provision of aviation safetyweather-related data for an aircraft 100 carrying the instrument 104 andfor use by other aircraft or aviation safety information consumers isone application of embodiments of the present disclosure, otherapplications may include placing a multifunctional system 104 insatellites, in space vehicles, in balloons, or in other vehicles orlocations, and with any number of different look angles in differentdirections.

FIG. 2 depicts an arrangement of components of a multifunctionalinstrument or system 104 in accordance with embodiments of the presentdisclosure. In general, the multifunctional instrument 104 includes alidar system 204. The lidar system 204 may be in the form of an opticalautocovariance wind lidar that incorporates a laser or light source 224and an interferometer 228. The laser 224 can output beams of light atmultiple wavelengths (λ₁, λ₂, . . . λ_(n)) within a time sequencedmanner, or simultaneously. Alternatively, multiple laser sources 224operating at different wavelengths can be provided. The lidar system 204can include a beam division system or mechanism 208 that operates toseparate output beams 116 of different wavelengths and direct theseparated beams 116 along different lines of sight within differentfields of regard 108. Moreover, the multifunctional instrument 104 caninclude scan mirrors, variable optics, or other scan mechanisms 212 forscanning an output beam 116 across a target volume 112, and forreceiving return signals 120 from along selected lines of sight withinthe field of regard 108 encompassing the target volume 112. Moreparticularly, the beam division system 208 operates to direct light ofdifferent wavelengths along different paths. A scan mechanism 212 can beprovided for each of the different paths (wavelengths). Accordingly, ascan mechanism 212 a-c can direct a respective beam of output light 116along a selected look angle within an associated field of regard 108a-c, and can further operate to receive returns 120 from within theassociated field of regard 108. Accordingly, scanning mechanisms 212 canscan the output beams 116 to obtain returns 120 from different locationswithin a target volume 112, such that measurements of wind speed orother phenomena can be made from select locations within the targetvolume 112.

In accordance with at least some embodiments of the present disclosure,the multifunctional instrument 104 includes components for detecting aproportion of cross-polarized light in the return signal 120. In suchembodiments, the multifunctional instrument 104 can include a polarizingbeam splitter 214 that sends co-polarized light included in the returnsignal 120 to the interferometer 228, and cross-polarized light to oneor more detectors 215. More particularly, one detector operable todetermine an intensity of the co-polarized light included in theinterferometer 228 and one detector 215 operable to determine anintensity of the cross-polarized light is provided for each wavelengthof interest.

The multifunctional instrument 104 also includes a wide field of viewinfrared camera 216. The infrared camera 216 can be operated to obtainspatial and temporal temperature information from within a relativelywide field of view 114. Moreover, the wide field-of-view infrared cameracan be pointed so as to encompass the forward-looking field-of-regard108 a of the lidar (see, e.g., FIG. 1), and can be used to measurespatial and temporal temperatures and atmospheric conditions such asturbulence. As an example, but without limitation, the infrared camera216 may comprise a wide field of view infrared sensor for measuring thespatial and temporal temperatures and atmospheric conditions such asturbulence and providing a large area of surveillance over a widewavelength range (e.g. 7.5 to 14 μm). For example, the infrared camera216 can detect the presence of clouds and potential turbulent activityalong the direction of travel of the aircraft 100, and such informationcan be used as an input for making aviation safety weather-relatedpredictions. In addition, such information can be used to assist insteering an output beam 116 of the lidar system 204. As an alternativeor in addition to an infrared camera 216, a hyperspectral ormultispectral instrument, including an instrument with a wide field ofview, can be included in the multifunctional instrument 104.

In accordance with further embodiments of the present disclosure, themultifunctional instrument 104 can include an accelerometer 220, whichcan be operated to measure the intensity of turbulence experienced bythe aircraft 100, and to provide a correlation between turbulencepredictions made through operation of the lidar system 204 and actualturbulence conditions experienced by the aircraft 100.

Embodiments of the multifunctional instrument 104 described hereinadditionally include an inertial navigation unit (INU) 232, such as butnot limited to a global positioning system (GPS) INU, which can operateto provide aircraft 100 location information. Such information can beused to support various functions, including but not limited togeo-locating detected or predicted aviation safety related weatherconditions.

The various sensors and instruments such as the lidar system 204, thewide field of view camera 216, the accelerometer 220, the beam division208 and scanning 212 systems, and the related mechanisms of themultifunctional instrument 104 can all be interconnected to a controlsystem 222. As discussed in greater detail elsewhere herein, the variouscomponents can work in conjunction with one another and the controlsystem 222 to make measurements of atmospheric conditions, and to makepredictions regarding the presence of turbulence in the atmosphere,including but not limited to along the direction of motion of theaircraft 100, to correlate windspeed and temperature measurements andrelated turbulence predictions to turbulence actually experienced by theaircraft 100, to detect the presence of icing conditions, to detect thepresence of volcanic ash or other particles, and to provide such orother information that is pertinent to aviation safety or navigation ordetected weather conditions, to other aircraft, aviation safety relatedweather information consumers, or general weather information consumers.

The control system 222 of the multifunctional instrument 104 can includevarious processing and operating components, including but not limitedto a processor 236, memory 240, and a communications interface 244. Ascan be appreciated by one of skill in the art after consideration of thepresent disclosure, the processor 236 can include a general purposeprogrammable processor, a graphics processing unit (GPU), a fieldprogrammable gate array (FPGA), a controller, or a set of differentprocessor devices or chips. The memory 240 can include solid-statevolatile or non-volatile memory, such as flash memory, RAM, DRAM, SDRAM,or the like. The memory 240 can also include various other types ofmemory or other data storage devices, such as magnetic storage devices,optical storage devices, or the like.

The processor 236 can generally operate to execute programming code orinstructions stored in the memory 240, for the operation of themultifunctional instrument 104, including coordination of the operationof components within the multifunctional system 104. Moreover, theprocessor 236 can execute application programming or instructions storedin the memory 240 for the onboard prediction of aviation safety relatedweather conditions, and improved flight navigation paths including butnot limited to the detection of clear air turbulence along the path ofthe aircraft 100. In accordance with still other embodiments of thepresent disclosure, such predictions can be made in connection with windspeed measurements taken by the lidar system 204 along lines of sightother than those within the forward-looking field of regard 108 a, suchas a downward looking field of regard 108 b, or an upward looking fieldof regard 108 c. The measurements can provide shear information relatedto potential turbulence or enhanced aircraft navigation and fuelefficiency. Data collected or generated by the sensors of themultifunctional instrument 104 can be stored in the memory 240,presented to the crew of the aircraft 100, or communicated using thecommunication interface 244 to other systems, such as aviation safety ornavigation related weather information consumers, other aircraft,weather services, or the like.

An example of application programming or instructions that can be storedin the memory 240 and executed by the processor 236 is a deep learningalgorithm 242. The deep learning algorithm 242 can operate to collect,fuse, and correlate data generated by the multifunction sensor 104, theinfrared camera 216, the accelerometers 220, and external sources. Thedeep learning algorithm 242 can apply the data to make predictionsregarding turbulence and other wind-based aviation safety and efficiencyphenomena. This data can also be used to train the deep learningalgorithm 242 to enable increasingly accurate predictions of wind basedaviation safety phenomena or wind-aided navigation and efficiency. Inaddition, embodiments of the present disclosure can provide a deeplearning algorithm 242 that can alter, or suggest alterations in, thecourse of the aircraft 100, in order to avoid turbulence or other windbased aviation safety or navigation phenomena.

As previously noted, in at least some embodiments of the presentdisclosure, the output beams 116 of the different fields of regard 108are associated with different wavelengths. In such embodiments, aninterferometer 228 capable of operating at different wavelengthssimultaneously can be used. The components of such an interferometer 228are depicted in FIG. 3. In this example, a dual wavelengthinterferometer 228 is illustrated and described. However, as can beappreciated by one of skill in the art after consideration of thepresent disclosure, the interferometer 228 can be configured to operateat a single wavelength or at more than two wavelengths. In general, theinterferometer 228 receives dual wavelength light as an input. The lightcan comprise a time t0 sample of light output by the light source 224,and a time t>0 signal comprising the return signal 120 collected by thelidar system 204. The light is passed to the interferometer 228 by atransmission element 302, such as a fiber optic element and/or turningmirror, that delivers light of a mix of different polarizations to theinterferometer 228. In accordance with embodiments of the presentdisclosure, the interferometer system or instrument 228 may include afirst single or dual-wavelength non-polarizing beam splitter 304 thatdirects or transmits a first portion 308 of the received light to afirst arm 312 and a second portion 316 of the received light 300 to asecond arm 320 of the interferometer 228.

The first arm 312 includes a first reflective element 324 that is afirst distance from the first non-polarizing beam splitter 304. Thefirst reflective element 324 reflects light of a first wavelength 328and transmits light of a second wavelength 332. The first reflectiveelement 324, optionally in combination with a secondary mirror 344,defines a first optical path length for light of the first wavelength328 included in the portion of light directed to the first arm 312. Inaccordance with embodiments of the present disclosure, the firstreflective element 324 is a frequency selective mirror or dichroicelement. The first arm 312 further includes a second reflective element336 that is a second distance from the first non-polarizing beamsplitter 304, where the second distance is greater than the firstdistance. The second reflective element 336 reflects light of the secondwavelength 332. The second reflective element 336, optionally incombination with the same secondary mirror 344, defines a second opticalpath length for light of the second wavelength 332 included in theportion of light directed to the first arm 312.

The second arm 320 includes a third reflective element 340 that is athird distance from the first non-polarizing beam splitter 304, wherethe third distance is less than either of the first and seconddistances. The third reflective element 340, optionally in combinationwith a secondary mirror 348, defines a third optical path length for thelight of the first and second wavelengths included in the portion of thelight directed to the second arm 320.

The first 312 and second 320 arms may be configured as cat-eyeassemblies with reflective elements 324, 336, and 340 that comprisenon-planar, for example parabolic, mirrors that are combined withsecondary mirrors 344 and 348 to provide a compact physical structurethat provides an optical path difference for rays within a given one ofthe arms 312 and 320 that is essentially constant for all rays of agiven wavelength within the field of view of the interferometer 228,regardless of the angle at which the rays entered the assembly. Systemsand methods for providing such a field widening lens are described inU.S. Pat. No. 7,929,215, the contents of which are incorporated hereinby reference in their entirety.

In accordance with further embodiments of the present disclosure, one ofthe arms 312 or 320 of the interferometer 228 includes a quarter waveplate 352 for introducing a delay to light of a linear polarization. Thequarter wave plate 352 can be in, for example, the optical pathtraversed by the light directed along the first arm 312 of theinterferometer 228.

Light at one or both wavelengths from the first 312 and second 320 armsis combined at a second non-polarizing beam splitter 356. A firstportion 360 of the combined light is directed (e.g. is passed) by thesecond non-polarizing beam splitter 356 to a first wavelength selectiveor dichroic element 364, while a second portion 368 of the combinedlight is directed (e.g. is reflected) by the second non-polarizing beamsplitter 356 to a second wavelength selective or dichroic element 372.

Light of the first wavelength is reflected by the first wavelengthselective element 364 to a first polarizing beam splitter 376 a, whilelight of the second wavelength is passed by the first wavelengthselective element 364 to a second polarizing beam splitter 376 b. Lightof the first wavelength is reflected by the second wavelength selectiveelement 372 to a third polarizing beam splitter 376 c, while light ofthe second wavelength is passed by the second wavelength selectiveelement 372 to a fourth polarizing beam splitter 376 d. In accordancewith embodiments of the present disclosure, each of the first throughfourth polarizing beam splitters 376 is associated with first and seconddetectors 380. Moreover, a portion of the light received at each of thedetectors has been delayed by a selected amount within the instrumentrelative to other light. The detectors 380 may comprise photodetectorsthat are operative to detect an amplitude (intensity) of light incidentthereon. Moreover, the detector electronics assemblies 380 can beselected and configured to operate at speeds that are fast enough toresolve returns from different ranges, and thus from different portionsof the target volume 112.

Specifically, light of the first wavelength that has traversed the firstpath length in the first arm 312 is combined with the light of the firstwavelength that has traversed the third path length in the second arm320, thus creating an interference pattern. The intensity of theinterference pattern is measured at each of the detectors 380 associatedwith the first 376 a and third 376 c polarizing beam splitters, wherethe phase of the signals received at each of the detectors 380 are,through the combination of transmitting and reflecting elements withinthe interferometer 228, spaced in phase from neighboring signals of thesame wavelength by 90 degrees. Similarly, light of the second wavelengththat has traversed the second path length in the first arm 312 iscombined with the light of the second wavelength that has traversed thethird path length in the second arm 320, and the intensity of theinterference pattern is measured by each of the detectors 380 associatedwith the second 376 b and fourth 376 d polarizing beam splitters, wherethe interference pattern signals received at each of the detectors 380are spaced in phase from the other signals of the same wavelength by anominal 90 degrees. Analysis can then be performed on the signals fromeach set of detectors (one set per wavelength) to determine a phase ofthe interferometer fringe (measured autocovariance function) of thelight relative to the four detector phase positions. More particularly,the phase analysis procedure can be performed for each of thewavelengths at times t0 and t>0 to determine a relative phase change ofthe interferometer fringe (measured autocovariance function) of thelight, from which a line of sight velocity of the atmosphericconstituents from which the return light 120 was reflected may beretrieved. As can be appreciated by one of skill in art afterconsideration of the present disclosure, the measured relative phasechange can then be used to determine the relative line of sight windspeed within the target volume 112 at a selected range.

In accordance with further embodiments of the present disclosure, thepolarization of light received as part of a return signal 120 can bedetermined. In such embodiments, the transmitted beam 116 may becontrolled to have a selected polarization. The intensity or amount ofco-polarized light relative to the intensity or amount orcross-polarized light in the return signal 120 can then be determinedfor at least one of the wavelengths of light in the return signal 120.For example, a polarizing beam splitter 214 can be provided to dividelight included in the return signal 120 into a co-polarized portion thatis provided to the interferometer 228, and a cross-polarized portionthat is provided to a detector 215. A large proportion of crosspolarized light relative to co-polarized light in the return signal 120indicates that ice, ash, or dust particles are present within the targetvolume 112. These measurements can be correlated with temperaturemeasurements, for example taken by the infrared camera 216, to indicatethe presence of icing conditions, volcanic ash, or other relevantconditions. Different proportions of cross polarized and co-polarizedlight (into the interferometer) in the return signal 120 can alsoindicate aerosol properties within the target volume 112.

As depicted, the multifunctional instrument 104 can be associated withmultiple fields of regard, with multiple pointing angles of the lidarbeam being included within each field of regard. For example, a firstfield of regard 108 a can be directed so as to obtain measurements fromahead of the aircraft 100. This first field of regard 108 a can operatein connection with an output beam 116 having a first wavelength. Anexample of a suitable wavelength is 355 nm, which is suitable formeasuring winds and clear air turbulence in a direction forward of theaircraft 100 motion. A second field of regard 108 b can be pointed in adownward direction, to obtain measurements from altitudes below theaircraft flight altitude. This second field of regard 108 b can operatein connection with an output beam 116 having a second wavelength. Anexample of a suitable wavelength for a downward looking field of regard108 b is 1.5 μm, which is suitable for measuring winds in regions withhigher aerosol/particle concentration including in clouds. A third fieldof regard 108 c can be pointed upward, to obtain measurements fromhigher altitudes. This third field of regard 108 c can operate inconnection with an output beam 116 c having a third wavelength. Exampleof suitable wavelengths for an upward looking field of regard 108 c are355 nm and 532 nm, both of which are suitable for measuring returnsproduced by molecules at high altitudes. Alternatively, the operationalwavelengths can be limited to those that comply are eye-safe. Moreover,the transmitted beam associated with a given field of regard 108 can bescanned to widen the area from which measurements are taken.

In accordance with other embodiments of the present disclosure,measurements of wind speed within target volumes 112 associated withdifferent fields of regard 108 can be obtained in a time sequencedmanner, rather than simultaneously. Moreover, in accordance with atleast some embodiments of the present disclosure, an interferometer 228that provides different optical path differences to light of differentwavelengths is not required. Measurements taken by the lidar system 204can be used in combination with measurements taken by the wide angleinfrared camera 216. Moreover, measurements taken by one of theinstruments 204 or 216 can be used to determine operating parameters ofthe other instrument. For example, the look angle of the lidar system204 can be selected based on the determined location of clouds detectedby the infrared camera 216.

In accordance with embodiments of the present disclosure, in addition toa forward pointing field of regard 108 a, information relative toturbulence that might affect the aircraft 100 can be obtained fromdownward looking 108 b and/or upward looking 108 c fields of view. Forexample, turbulence is indicated by the presence of different windshaving different directions at different, adjacent altitudes. Inaddition, by enabling the detection of wind speeds at altitudes aboveand below the aircraft 100, embodiments of the present disclosure canfacilitate the selection of an altitude at which a tailwind component ispresent, facilitating fuel efficiency and speed.

In accordance with still other embodiments of the present disclosure,actual turbulence experienced by the aircraft 100, as measured by one ormore accelerometers 220, can be used to validate and/or refine thepredictions made based on measurements taken by the other components ofthe multifunction system 104. In addition to measurements taken by themultifunctional system 104 directly, weather information from othersources that may lead to turbulence can be validated based on theindication of turbulence experienced by the aircraft 100.

FIG. 4 depicts a process for applying deep learning processing to detectturbulence in accordance with embodiments of the present disclosure. Theprocess can be implemented by execution by the processor 236 of the deeplearning algorithm 242 stored in memory 240. The process includesreceiving and processing inputs from multiple data sources (step 404).These data sources can include inputs from a multifunctional system orinstrument 104. Specific examples of input data include, but are notlimited to, OAWL 204 based remotely sensed wind vector and clear airturbulence measurements, IR camera 216 based measurements of clouds, onboard turbulence intensity level detection signals from sensors, such asaccelerometers and aircraft eddy dissipation rate measurements, andexternal weather and turbulence forecasting data, such as graphicalturbulence guidance product (GTG) and now casting (e.g. NGTG).

Algorithmic input data fusion is then performed (step 408). Data fusioncan include correlating turbulence predictions made by execution of thedeep learning algorithm 242 based on measurements by the lidar system204 or other multifunctional instrument 104 sensors with actualturbulence encountered by the aircraft 100, for example as indicated byonboard accelerometers 220, or other sensors. In addition to temporalcorrelation, data fusion can include correlating the severity of thepredicted turbulence to the severity of the turbulence detected by theaircraft at various altitudes and relative air speeds of turbulenceencounters. Other examples of data fusion include correlating externalweather and turbulence forecasting data with turbulence predictions madeby the multifunction system 104 and/or actual turbulence measurementsmade by sensors included in the multifunction system 104. In accordancewith further embodiments of the present disclosure, data fusion relativeto atmospheric conditions other than or in addition to clear airturbulence can be performed. For example, correlations betweenpredictions regarding icing conditions, the presence of organic ash, orother particles in the atmosphere and the conditions actuallyencountered by the aircraft 100 can be made. Data regarding thepolarization of backscattered return laser light 120 and the detectionof clouds using the infrared camera 216 are examples of sources of dataregarding predictions of such other atmospheric conditions.

In a training mode, fused data can be used to train a deep learningmodel implemented by the learning algorithm 242 (step 412). In deeplearning, the fused data is fed into the model and used to refine thepredictions made regarding the atmospheric parameters of interest, suchas clear air turbulence. More particularly, by comparing the data usedto make the predictions with the actual turbulence measurements,refinements to the model or algorithm 242 to increase the accuracy ofthe predictions can be made. For instance, if the model implemented bythe algorithm 242 predicts that turbulence of a certain predictedseverity will be encountered at a particular range, based onmeasurements made by the multifunction sensor 104 from or about thatrange, the actual severity of any turbulence encountered when theaircraft 100 has reached the site of the predicted turbulence can beused to adjust the model of the algorithm 242 so that future predictionsare more accurate. As can be appreciated by one of skill in the artafter consideration of the present disclosure, actual turbulencemeasurements made by sensors, such as accelerometers 220 carried by theaircraft 100 as part of the multifunction sensor 104, can be locatedtemporally, using clock information, and spatially, using thegeolocation data, for example from an INU 232 included in themultifunction sensor 104. Such predictions can be refined to includecharacteristics of the turbulence predicted by the multifunction sensor104 and the effects of turbulence on the particular aircraft 100 atvarious altitudes and air-speeds. Alternatively or in addition,turbulence predictions based on external weather and turbulenceforecasting data alone or in combination with the sensor data regardingactual turbulence can be refined through the training of the algorithm242. In accordance with embodiments of the present disclosure, themachine learning process includes supervised learning, with thealgorithm 242 being trained to accurately detect the presence andseverity of turbulence or other aviation safety weather-relatedparameters. In accordance with still other embodiments the presentdisclosure, the machine learning process can include reinforcementlearning, in which feedback regarding the accuracy of predictions madefrom input data by the algorithm 242 is checked against measurements ofactual instantiations of the predicted phenomenon, to allow thealgorithm 242 performance to be continually improved.

The training process results in deep learning models that are betterable to predict, based on the various inputs, such as data collected bythe multifunction sensor 104 alone or in combination with data fromexternal weather forecasting services or other instruments, the presentclear air turbulence, or other weather conditions of interest.Accordingly, at step 416, the trained deep learning model 242 can beapplied to predict output information with improved accuracy. The outputinformation can comprise deep learning real time output data (step 420),which can include enhanced wind vector values, enhanced clear airturbulence intensity values, and enhanced correlation between OAWLturbulence detection by the multifunction sensor 104 and the sensing byaccelerometers 220 as the aircraft 100 flies through the turbulent path.

In accordance with further embodiments of the present disclosure, thereinforcement learning models can be used to incorporate the fused data,and predict optimized courses of action for the aircraft 100 and/or themultifunction sensor 104 (step 424). This reinforcement learning realtime output data (step 428) can include action to change the laser rangegate for measurements made by the lidar system 204, and action to informa pilot or an autopilot system to change flight parameters or to staythe course as the best reaction to predicted weather conditions.

The reinforcement learning models or algorithms 242 can include a numberof different variants, such as Q-Learning,State-Action-Reward-State-Action (SARSA), Deep Q Network (DQN), and DeepDeterministic Policy Gradient (DDPG).

The execution of algorithms 242 implementing the deep learning model forprocessing data input from the multifunction sensor 104 and other datasources can be performed by the processor 236 included in themultifunctional instrument 104. Accordingly, embodiments of the presentdisclosure provide an onboard processing solution. In addition,turbulence and other pertinent weather information can be provided inreal time or near real-time (e.g. after a processing delay of less thanone second), to enable the flight parameters of an aircraft 100 to beadjusted in response to the predicted weather conditions. Moreover, theoutput of the algorithm 242 can include instructions or suggestionsregarding actions in the form of flight parameters adjustments that canbe made to minimize the effect of the predicted weather condition. Inaddition to increasing the accuracy of predictions through training,embodiments of the present disclosure enable the integration and fusionof data from multiple sources to further increase the accuracy ofweather forecasting information, including predictions of clear airturbulence, provided by the algorithm 242.

Embodiments of the present disclosure can therefore include amultifunctional instrument 104 that incorporates optical autocovariancewind lidar-based instruments in combination with wide field of viewcameras or sensors. For example, a lidar system 204 comprising an OAWLinstrument having multiple lines of sight 108 can be included in themultifunctional instrument 104 for wind measurement and aerosolcharacterization, and a wide field of view IR sensor 216 can be includedin the multifunctional instrument 104 for thermal measurement ofatmospheric behavior. In accordance with still other embodiments, amultifunctional instrument 104 can additionally include on-boardturbulence intensity level detection instruments, such as one or moreaccelerometers 220 for measuring turbulence for an aircraft 100 whichincludes platforms not limited to airplanes, helicopters, airships(including blimps), gliders, hot air balloons and Unmanned AerialVehicles (UAVs) carrying the multifunctional instrument 104.Accordingly, a multifunctional instrument 104 as described herein canprovide enhanced measurements of clear air turbulence and otherweather-based aviation safety and navigation phenomena (volcanic ash,icing conditions). Moreover, by incorporating more than one instrumentor sensor, a multifunctional instrument 104 as described herein providesmulti-source, diverse data sets for increasing the accuracy of andfinding correlations in aviation safety weather-related predictions, andfor providing wind-aided navigation information and guidance.

In addition, a multifunctional instrument 104 in accordance withembodiments of the present disclosure can provide multiple field ofregard 108 OAWL instrument configurations which may incorporate multiplewavelengths, increasing the data diversity for aviation safetyweather-related predictions. As examples, but without limitation, alidar system 204 included in a multifunctional instrument 104 inaccordance with embodiments of the present disclosure can feature afirst field of regard 108 a comprising a horizontal LOS for measuringwinds and clear air turbulence using an output beam 116 a having a firstwavelength (λ₁)(e.g., 355 nm), a second field of regard 108 b comprisinga down-looking LOS for measuring winds in clouds using an output beam116 b having a second wavelength (λ₂) (e.g., 1.5 micron), and a thirdfield of regard 108 c comprising an up-looking LOS for measuring windsusing an output beam 116 c having a third wavelength (λ₃) (e.g., 532nm). In addition, some or all of the fields of regard 108 can beassociated with or established by a scanning mechanism 212, such as butnot limited to a conical scan mechanism that varies a field of view or aline of sight of a lidar system 204.

In addition to one or more lidar systems 204, a multifunctionalinstrument 104 in accordance with embodiments of the present disclosurecan include a wide FOV IR sensor or camera 216 for measuring the spatialand temporal temperatures in atmospheric conditions such as turbulenceand providing a large area of surveillance over a wide wavelength band(e.g., 7.5 to 14 microns) for supporting weather-related predictions foraviation safety. Alternatively, or in addition, a camera 216 can beutilized to detect cloud formations or other phenomena, to enable thelidar system 204 to scan areas without cloud formations. Moreover,on-board turbulence intensity level detection sensors or accelerometers220 can be included to provide correlation between OAWL turbulencedetection and the amplitude of its impact for the given altitude and airspeed conditions based on what is sensed as the aircraft 100 fliesthrough the turbulent path.

Still further embodiments of the present disclosure provide amultifunctional instrument 104 that provides unique and novel methods ofcombining advanced data fusion, deep learning, and reinforcementlearning algorithms simultaneously into a hybrid model to providemeasurements from the atmosphere, and predicted conditions based on suchmeasurements. For example, a multifunctional instrument 104 inaccordance with embodiments of the present disclosure can include a deeplearning algorithm 242 that, based on information from sensors includedin the multifunctional instrument 104, provide as outputs enhanced windvector values, enhanced clear air turbulence intensity values, enhancedcorrelation between OAWL turbulence detection and sensing byaccelerometers 220 as the aircraft 100 flies through the turbulent path,and/or reinforcement learning-based optimized course of action, such asactions to change a laser range gate of a lidar system 204, to inform apilot or auto pilot to change a flight path or stay the course, and thelike. Moreover, as can be appreciated by one of skill in the art afterconsideration of the present disclosure, optimization of a lidar system204 range gate enhances the efficient collection of data. For instance,when the strength of a return signal 120 is low, the range gate lengthalong which wind speed measurements are made can be increased, whichdecreases range resolution, but increases sensitivity.

Embodiments of the present disclosure incorporating deep learning canprovide continuously learned spatial and temporal analytics. Inparticular, the model implemented by the algorithm 242 can identify andlearn from recurring weather patterns over time including weatherpatterns in the form of data collected by a multifunctional instrument104 as described herein. The deep learning can also address spatialcorrelation, including the spatial dynamic influence of atmospherics onweather phenomena and associated predictions provided as output by thealgorithm 242. Embodiments of the present disclosure can additionallyincorporate reinforcement learning. As a result, embodiments of thepresent disclosure can provide for optimized courses of action inguidance provided regarding an optimal flight path for an aircraft 100,and for the operation of instruments and sensors included in amultifunctional instrument 104 as disclosed herein.

Although various embodiments of a multifunctional instrument 104 havingparticular features have been described, other configurations arepossible. For example, different interferometer 228 configurations canbe incorporated into the multifunctional instrument 104. For instance,rather than incorporating a single interferometer 228 capable ofoperating at a plurality of wavelengths, a plurality of interferometers228 that each operate at a single wavelength can be included. As anotherexample, interferometers for handling different wavelengths and that arecapable of handling different numbers of wavelengths can be included inany combination.

As another example, an interferometer 228 included in a multifunctionalinstrument in accordance with embodiments of the present disclosure neednot incorporate a field widening lens arrangement. For instance, ratherthan a set of mirrors, the interferometer 228 can include a hexagonalbeam splitter.

The information available from a multifunctional instrument 104 asdescribed herein can include data collected from returns at multiplewavelengths indicating the presence, magnitude, and direction ofatmospheric winds, from within multiple fields of regard at differentangles relative to the instrument. The data can additionally includeinformation regarding the presence of ice, ash, or dust particles in theatmosphere. Moreover, information regarding the presence and location ofclouds can be obtained. The data collected by the multifunctionalinstrument 104 can be processed using artificial intelligence-based deepand reinforcement learning processing algorithms 242 to providereal-time and near-real time weather predictions, wind-aided navigation,turbulence predictions, and/or courses of action for use by an aircraft100 carrying the multifunctional instrument 104, by other aircraft, orby other data consumers. Predictions and forecasts regardingmeasurements made using remote sensing instruments included in themultifunctional instrument 104 can be validated against measurements ofactual conditions, for example as detected by other sensors orinstruments, including but not limited to an accelerometer 220 or theperceptions of a pilot of the aircraft 100. Moreover, validation resultscan be used to refine the training and operation of the algorithm 242.In addition to providing information useful to ensuring a smooth andsafe flight, prediction and measurements made by a multifunctionalinstrument 104 in accordance with embodiments of the present disclosurecan aid in efficiency, for instance by assisting the aircraft 100 inlocating altitudes at which favorable wind conditions are present.

The foregoing discussion of the disclosed systems and methods has beenpresented for purposes of illustration and description. Further, thedescription is not intended to limit the disclosed systems and methodsto the forms disclosed herein. Consequently, variations andmodifications commensurate with the above teachings, within the skill orknowledge of the relevant art, are within the scope of the presentdisclosure. The embodiments described hereinabove are further intendedto explain the best mode presently known of practicing the disclosedsystems and methods, and to enable others skilled in the art to utilizethe disclosed systems and methods in such or in other embodiments andwith various modifications required by the particular application oruse. It is intended that the appended claims be construed to includealternative embodiments to the extent permitted by the prior art.

1. A multifunctional instrument, comprising: a laser source; aninterferometer; a beam division mechanism, wherein the beam divisionmechanism directs light from the laser source to a first field ofregard, wherein the beam division mechanism directs light from the lasersource to a second field of regard, wherein the beam division mechanismdirects light from within the first field of regard to theinterferometer, and wherein the beam division mechanism directs lightfrom within the second field of regard to the interferometer.
 2. Themultifunctional instrument of claim 1, wherein the beam divisionmechanism directs light from within the first field of regard and lightfrom within the second field of regard to the interferometer atdifferent times.
 3. The multifunctional instrument of claim 1, whereinthe laser source outputs light having a first wavelength that isdirected to the first field of regard, wherein the laser source outputslight having the same or a second wavelength that is directed to thesecond field of regard, and wherein light from the first field of regardand light from the second field of regard are provided to theinterferometer simultaneously.
 4. The multifunctional instrument ofclaim 3, wherein the interferometer provides a first optical pathdifference for light of the first wavelength and a second optical pathdifference for light of the second wavelength.
 5. The multifunctionalinstrument of claim 1, further comprising: an infrared camera, whereinthe infrared camera has a field of view that encompasses at least thefirst field of regard of the optical autocovariance lidar.
 6. Themultifunctional instrument of claim 1, further comprising: a pluralityof detectors, wherein a first subset of the detectors receives light ofthe first wavelength, wherein a second subset of the detectors receiveslight of the second wavelength, wherein the light received at a firstdetector of the first subset of detectors is spaced in phase from thelight received at a second detector of the first subset of detectors bya nominal 90 degrees, and wherein the light received at a first detectorof the second subset of detectors is spaced in phase from the lightreceived at a second detector of the second subset of detectors by anominal 90 degrees.
 7. A multifunctional instrument, comprising: a lidarsystem, including: a first laser source; an interferometer; anddetectors; and a control system, including: memory, wherein a machinelearning algorithm is stored in the memory; and at least a processor,wherein the processor is operable to execute the machine learningalgorithm, wherein wind measurement data is collected by the lidarsystem and provided to the machine learning algorithm, wherein thealgorithm operates to predict turbulence or provide navigationinformation from the wind measurement data.
 8. The multifunctionalinstrument of claim 7, further comprising: an accelerometer, whereinactual turbulence measurement data is collected by the accelerometer andis provided as an input to the machine learning algorithm.
 9. Themultifunctional instrument of claim 8, wherein the machine learningalgorithm is trained using correlated wind measurement data and actualturbulence measurement data.
 10. The multifunctional instrument of claim7, wherein the interferometer includes at least first and second fieldsof regard.
 11. The multifunctional instrument of claim 7, wherein theinterferometer includes: a first, forward looking field of regard; asecond, upward looking field of regard; and a third, downward lookingfield of regard.
 12. The multifunctional instrument of claim 7, furthercomprising: an infrared camera.
 13. The multifunctional instrument ofclaim 7, wherein the machine learning algorithm is a deep neuralnetwork.
 14. A method of detecting turbulence in the atmosphere,comprising: making wind speed measurements along a series of anglescentered around the direction of travel of an aircraft; detectingturbulence experienced by the aircraft; correlating the wind speedmeasurements to the detected turbulence experienced by the aircraft; andtraining a machine learning algorithm using the correlated wind speedmeasurements and detected turbulence.
 15. The method of claim 14,wherein the machine learning algorithm is a deep neural network.
 16. Themethod of claim 14, wherein the wind speed measurements are obtainedfrom a plurality of different ranges along the direction of travel ofthe aircraft.
 17. The method of claim 16, wherein the turbulenceexperienced by the aircraft is detected by sensors carried by theaircraft, and wherein correlating the wind speed measurements to thedetected turbulence includes at least one of spatial correlation,temporal correlation, and amplitude correlation.
 18. The method of claim15, further comprising providing an output from the deep neural network,wherein the output is a turbulence prediction, wind-aided navigationinformation and a suggestion to alter a flight parameter of theaircraft.
 19. The method of claim 14, further comprising: taking windmeasurements along a plurality of look angles, wherein at least some ofthe look angles do not correspond to the direction of travel of theaircraft.
 20. The method of claim 14, further comprising: determining astrength of a return signal; in response to determining that thestrength of the return signal is low, increasing the range gate lengthalong which the wind speed measurements are made.